Search results for "FPCA"

A Data-Driven Approach for Studying the Influence of Carbides on Work Hardening of Steel

2022

This study proposes a new approach to determine phenomenological or physical relations between microstructure features and the mechanical behavior of metals bridging advanced statistics and materials science in a study of the effect of hard precipitates on the hardening of metal alloys. Synthetic microstructures were created using multi-level Voronoi diagrams in order to control microstructure variability and then were used as samples for virtual tensile tests in a full-field crystal plasticity solver. A data-driven model based on Functional Principal Component Analysis (FPCA) was confronted with the classical Voce law for the description of uniaxial tensile curves of synthetic AISI 420 ste…

Clustering of waveforms-data based on FPCA direction

2010

The necessity of nding similar features of waveforms data recorded for earthquakes at di erent time instants is here considered, since eventual similarity between these functions could suggest similar behavior of the source process of the corresponding earthquakes. In this paper we develop a clustering algorithm for curves based on directions de ned by an application of PCA to functional data.

Functional Principal components direction to cluster earthquake waveforms

2010

Looking for curves similarity could be a complex issue characterized by subjective choices related to continuous transformations of observed discrete data (Chiodi, 1989). In this paper we combine the aim of finding clusters from a set of individual curves to the functional nature of data, applying a variant of a k-means algorithm based on the principal component rotation of data. We apply a classical clustering method to rotated data, according to the direction of maximum variance. A k-means clustering algorithm based on PCA rotation of data is proposed, as an alternative to methods that require previous interpolation of data based on splines or linear fitting (Garc´ıa- Escudero and Gordali…

Functional Principal Component Analysis for the explorative analysis of multisite-multivariate air pollution time series with long gaps

2013

The knowledge of the urban air quality represents the first step to face air pollution issues. For the last decades many cities can rely on a network of monitoring stations recording concentration values for the main pollutants. This paper focuses on functional principal component analysis (FPCA) to investigate multiple pollutant datasets measured over time at multiple sites within a given urban area. Our purpose is to extend what has been proposed in the literature to data that are multisite and multivariate at the same time. The approach results to be effective to highlight some relevant statistical features of the time series, giving the opportunity to identify significant pollutants and…

FPCA Algorithm For Waveform Clustering

2011

Similar features between waveform data recorded for earthquakes at different time instants could suggest similar behavior of the source process of the corresponding source seismic process. In this paper we combine the aim of finding clusters from a set of individual waveform curves with the functional nature of data, applying a variant of a k-means algorithm based on the principal component rotation of data. This approach overcome the limitation of the cross-correlation, and represents an alternative to methods based on the interpolation of data by splines or linear fitting.

IMPLEMENTAZIONE DI UN ALGORITMO DI CLUSTERING PER L’IDENTIFICAZIONE DELLE PRECIPITAZIONI STRATIFORMI E CONVETTIVE ALLA SCALA D’EVENTO: UN’APPLICAZION…

Les cités de la gastronomie

2016

Long gaps in multivariate spatio-temporal data: an approach based on functional data analysis

2015

The main aim of this paper is to perform Functional Principal Component Analysis (FPCA) taking into account spatio-temporal correlation structures, in order to fill in missing values in spatio-temporal multivariate data set. A spatial and a spatio-temporal variant of the classical temporal FPCA is considered; in other words, FPCA is carried out after modeling data with respect to more than one dimension: space (long, lat) or space+time. Moreover, multidimensional FPCA is extended to multivariate context (more than one variable). Information on spatial or spatiotemporal structures are efficiently extracted by applying Generalized Additive Models (GAMs). Both simulation studies and some perfo…

Clustering of waveforms based on FPCA direction

2010

Looking for curves similarity could be a complex issue characterized by subjective choices related to continuous transformations of observed discrete data (Chiodi, 1989). Waveforms correlation techniques have been introduced to charac- terize the degree of seismic event similarity (Menke, 1999) and in facilitating more accurate relative locations within similar event clusters by providing more precise timing of seismic wave (P and S) arrivals (Phillips, 1997). In this paper functional analysis (Ramsey, and Silverman, 2006) is considered to highlight common characteristics of waveforms-data and to summarize these charac- teristics by few components, by applying a variant of a classical clust…

Comparing Spatial and Spatio-temporal FPCA to Impute Large Continuous Gaps in Space

2018

Multivariate spatio-temporal data analysis methods usually assume fairly complete data, while a number of gaps often occur along time or in space. In air quality data long gaps may be due to instrument malfunctions; moreover, not all the pollutants of interest are measured in all the monitoring stations of a network. In literature, many statistical methods have been proposed for imputing short sequences of missing values, but most of them are not valid when the fraction of missing values is high. Furthermore, the limitation of the methods commonly used consists in exploiting temporal only, or spatial only, correlation of the data. The objective of this paper is to provide an approach based …